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Important

IBM Quantum Platform is moving and this version will be sunset on July 1. To get started on the new platform, read the migration guide.

Experiment Results

qiskit.result

Result(backend_name, backend_version, ...[, ...])Model for Results.
ResultError(error)Exceptions raised due to errors in result output.
Counts(data[, time_taken, creg_sizes, ...])A class to store a counts result from a circuit execution.
marginal_counts(result[, indices, inplace, ...])Marginalize counts from an experiment over some indices of interest.
marginal_distribution(counts[, indices, ...])Marginalize counts from an experiment over some indices of interest.
marginal_memory(memory[, indices, ...])Marginalize shot memory

Distributions

ProbDistribution(data[, shots])A generic dict-like class for probability distributions.
QuasiDistribution(data[, shots, ...])A dict-like class for representing quasi-probabilities.

Expectation values

sampled_expectation_value(dist, oper)Computes expectation value from a sampled distribution

Mitigation

BaseReadoutMitigator()Base readout error mitigator class.
CorrelatedReadoutMitigator(assignment_matrix)N-qubit readout error mitigator.
LocalReadoutMitigator([assignment_matrices, ...])1-qubit tensor product readout error mitigator.
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